---
license: cc-by-nc-4.0
task_categories:
- image-classification
- text-to-image
library_name: datasets
tags:
- ai-generated-content
- image-quality-assessment
- real-vs-fake
- multimodal
---

# DANI: Discrepancy Assessing for Natural and AI Images

[Paper: D-Judge: How Far Are We? Evaluating the Discrepancies Between AI-synthesized Images and Natural Images through Multimodal Guidance](https://huggingface.co/papers/2412.17632)
[Code: https://github.com/RenyangLiu/DJudge](https://github.com/RenyangLiu/DJudge)

**A Large-Scale Dataset for Visual Research on AI-Synthesized and Natural Images**

## Overview

DANI (Discrepancy Assessing for Natural and AI Images) is a large-scale, multimodal dataset for benchmarking and broad visual research on both AI-generated images (AIGIs) and natural images.  
The dataset is designed to support a wide range of computer vision and multimodal research tasks, including but not limited to:
- AI-generated vs. real image discrimination
- Representation learning
- Image quality assessment
- Style transfer
- Image reconstruction
- Domain adaptation
- Multimodal understanding and beyond

DANI accompanies the paper:

> Liu, Renyang; Lyu, Ziyu; Zhou, Wei; Ng, See-Kiong.  
> *D-Judge: How Far Are We? Assessing the Discrepancies Between AI-synthesized Images and Natural Images through Multimodal Guidance.*  
> ACM International Conference on Multimedia (MM), 2025.

## Dataset Summary

DANI contains over **445,000 images**, including 5,000 natural images (from COCO, with resolutions 224, 256, 512, 1024) and more than 440,000 AI-generated images produced by diverse state-of-the-art generative models.  
Each sample is annotated with detailed metadata, enabling comprehensive evaluation and flexible use for a broad range of visual and multimodal research.
Images are generated using a wide range of generative models and protocols:

- **Models:** GALIP, DFGAN, SD_V14, SD_V15, Versatile Diffusion (VD), SD_V21, SD_XL, Dalle2, Dalle3, and COCO (real images)
- **Image Sizes:** 224, 256, 512, 768, 1024
- **Generation Types:** Text-to-Image (T2I), Image-to-Image (I2I), Text and Image-to-Image (TI2I)
- **Categories:** indoor, outdoor, etc.

## Data Fields

Each sample in the dataset contains the following fields:

| Field      | Description                                                                  |
|------------|------------------------------------------------------------------------------|
| index      | Unique index for each image                                                  |
| image      | The image itself (as a file, not just path)                                  |
| size       | Image resolution (e.g., 224, 256, 512, 768, 1024)                            |
| category   | Scene category (e.g., `indoor`, `outdoor`, etc.)                             |
| class_id   | COCO class or semantic category ID/name                                      |
| model      | Generative model used (`GALIP`, `DFGAN`, `SD_V14`, `SD_V15`, `VD`, etc.)     |
| gen_type   | Generation method (`T2I`, `I2I`, `TI2I`)                                     |
| reference  | Whether it is a real/natural image (`True` for real, `False` for generated)  |

> *Note:*  
> - **COCO** images have `reference=True`, and may appear at multiple resolutions.  
> - For AI-generated images, the `model` and `gen_type` fields indicate the specific generative model and generation protocol (T2I, I2I, or TI2I) used for each sample.

## Model/Generation Configurations

The dataset covers the following models and settings:

| Model    | Image Size | Generation Types Supported            |
|----------|------------|---------------------------------------|
| GALIP    | 224        | T2I                                   |
| DFGAN    | 256        | T2I                                   |
| SD_V14   | 512        | T2I, I2I, TI2I                        |
| SD_V15   | 512        | T2I, I2I, TI2I                        |
| VD       | 512        | T2I, I2I, TI2I                        |
| SD_V21   | 768        | T2I, I2I, TI2I                        |
| SD_XL    | 1024       | T2I, I2I, TI2I                        |
| Dalle2   | 512        | T2I, I2I                              |
| Dalle3   | 1024       | T2I                                   |
| COCO     | 224,256,512,1024 | Reference/Real Images           |

For each generation type (`T2I`, `I2I`, `TI2I`), a diverse set of models are covered.

## Usage

You can load DANI directly using the 🤗 datasets library:

```python
from datasets import load_dataset

ds = load_dataset("Renyang/DANI")
print(ds)
# Output: DatasetDict({
#     train: Dataset({
#         features: ['index', 'image', 'size', 'category', 'class_id','model', 'gen_type','reference'],
#         num_rows: 540257
#     })
# })
# Access images and metadata
img = ds["train"][0]["image"]
meta = {k: ds["train"][0][k] for k in ds["train"].column_names if k != "image"}

```

*Note:* Images are loaded as PIL Images. Use `.convert("RGB")` if needed.

## Citation

If you use this dataset or the associated benchmark, please cite:

```bibtex
@inproceedings{liu2024djudge,
  title = {D-Judge: How Far Are We? Assessing the Discrepancies Between AI-synthesized Images and Natural Images through Multimodal Guidance},
  author = {Liu, Renyang and Lyu, Ziyu and Zhou, Wei and Ng, See-Kiong},
  booktitle = {ACM International Conference on Multimedia (MM)},
  organization = {ACM},
  year = {2025},
}
```

## License

This dataset is released under the [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) license (for non-commercial research use).

## Contact

For questions or collaborations, please visit [Renyang Liu's homepage](https://ryliu68.github.io/).